A PyTorch implementation of "Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights"

@inproceedings{zhou2017, title={Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights}, author={Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen}, booktitle={International Conference on Learning Representations,ICLR2017}, year={2017}, } 

[Paper]

Official Caffe implementation is available [here] (Code in this repo is based on the paper and not on the official Caffe implementation)

The code is implemented in Python 3.7 and PyTorch 1.1

pip install -e . 

inq.SGD(...) implements SGD that only updates weights according to the weight partitioning scheme of INQ.

inq.INQScheduler(...) handles the the weight partitioning and group-wise quantization stages of the incremental network quantization procedure.

reset_lr_scheduler(...) resets the learning rate scheduler.

The INQ training procedure looks like the following:

optimizer = inq.SGD(...) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, ...) inq_scheduler = inq.INQScheduler(optimizer, [0.5, 0.75, 0.82, 1.0], strategy="pruning") for inq_step in range(3): # Iteration for accumulated quantized weights of 50% 75% and 82%  inq.reset_lr_scheduler(scheduler) inq_scheduler.step() for epoch in range(5): scheduler.step() train(...) inq_scheduler.step() # quantize all weights, further training is useless validate(...)

examples/imagenet_quantized.py is a modified version of the official PyTorch imagenet example. Using this example you can quantize a pretrained model on imagenet. Compare the file to the original example to see the differences.

Results using this code differ slightly from the results reported in the paper.